Satellite-based monitoring of pasture degradation on the Tibetan Plateau: A multi-scale approach

The Tibetan Plateau has been entitled Third-Pole-Environment'' because of its outstanding importance for the global climate and the hydrological system of East and Southeast Asia. Its climatological and hydrological influences are strongly affected by the local vegetation which is supposed to be subject to ongoing degradation. The degradation of the Tibetan pastures was investigated on the local scale by numerous studies. However, because methods and scales substantially differed among the previous studies, the overall pattern of degradation on the Tibetan Plateau is hitherto unknown. Consequently, the aims of this thesis are to monitor recent changes in the grassland degradation on the Tibetan Plateau and to detect the underlying driving forces of the observed changes. Therefore, a comprehensive remote sensing based approach is developed. The new approach consists of three parts and incorporates different spatial and temporal scales: (i) the development and testing of an indicator system for pasture degradation on the local scale, (ii) the development of a MODIS-based product usable for degradation monitoring from the local to the plateau scale, and (iii) the application of the new product to delineate recent changes in the degradation status of the pastures on the Tibetan Plateau. The first part of the new approach comprised the test of the suitability of a new two-indicator system and its transferability to spaceborne data. The indicators were land-cover fractions (e.g.,~green vegetation, bare soil) derived from linear spectral unmixing and chlorophyll content. The latter was incorporated as a proxy for nutrient and water availability. It was estimated combining hyperspectral vegetation indices as predictors in partial least squares regression. The indicator system was established and tested on the local scale using a transect design and textit{in situ} measured data. The promising results revealed clear spatial patterns attributed to degradation, indicating that the combination of vegetation cover and chlorophyll content is a suitable indicator system for the detection of pasture degradation on local scales on the Tibetan Plateau. To delineate patterns of degradation changes on the plateau scale, the green plant coverage of the Tibetan pastures was derived in the second part. Therefore, an upscaling approach was developed. It is based on satellite data from high spatial resolution sensors on the local scale (WorldView-type) via medium resolution data (Landsat) to low resolution data on the plateau scale (MODIS). The different spatial resolutions involved in the methodology were incorporated to enable the cross-validation of the estimations in the new product against field observations (over 600 plots across the entire Tibetan Plateau). Four methods (linear spectral unmixing, spectral angle mapper, partial least squares regression, and support vector machine regression) were tested on their predictive performance for the estimation of plant cover and the method with the highest accuracy (support vector machine regression) was applied to 14 years of MODIS data to generate a new vegetation coverage product. In the third part, the changes in vegetation cover between the years 2000 and 2013 and their driving forces were investigated by comparing the trends in the new vegetation coverage product against climate variables (precipitation from tropical rainfall measuring mission and 2 m air temperature from ERA-Interim reanalysis data) on the entire Tibetan Plateau. Large areas in southern Qinghai were identified where vegetation cover increased as a result of positive precipitation trends. Thus, degradation did not proceed in these regions. Contrasting with this, large areas in the central and western parts of the Tibetan Autonomous Region were subject to an ongoing degradation. This degradation can be attributed to the coincidence of rising temperatures and anthropogenic induced increases in livestock numbers as a consequence of local land-use change. In those areas, the ongoing degradation influenced local precipitation patterns because sensible heat fluxes were accelerated above degraded pastures. In combination with advected moist air masses at higher atmospheric levels, the accelerated heat fluxes led to an intensification of local convective rainfall. The ongoing degradation detected by the new remote sensing approach in this thesis is alarming. The affected regions encompass the river systems of the Indus and Brahmaputra Rivers, where the ongoing degradation negatively affects the water storage capacities of the soils and enhances erosion. In combination with the feed-back mechanisms between plant coverage and the changed precipitation on the Tibetan Plateau, the reduced water storage capacity will exacerbate runoff extremes in the middle and lower reaches of those important river systems

Remote Sensing of Plant Biodiversity

This Open Access volume aims to methodologically improve our understanding of biodiversity by linking disciplines that incorporate remote sensing, and uniting data and perspectives in the fields of biology, landscape ecology, and geography. The book provides a framework for how biodiversity can be detected and evaluated—focusing particularly on plants—using proximal and remotely sensed hyperspectral data and other tools such as LiDAR. The volume, whose chapters bring together a large cross-section of the biodiversity community engaged in these methods, attempts to establish a common language across disciplines for understanding and implementing remote sensing of biodiversity across scales. The first part of the book offers a potential basis for remote detection of biodiversity. An overview of the nature of biodiversity is described, along with ways for determining traits of plant biodiversity through spectral analyses across spatial scales and linking spectral data to the tree of life. The second part details what can be detected spectrally and remotely. Specific instrumentation and technologies are described, as well as the technical challenges of detection and data synthesis, collection and processing. The third part discusses spatial resolution and integration across scales and ends with a vision for developing a global biodiversity monitoring system. Topics include spectral and functional variation across habitats and biomes, biodiversity variables for global scale assessment, and the prospects and pitfalls in remote sensing of biodiversity at the global scale

Remote Sensing of Plant Biodiversity

At last, here it is. For some time now, the world has needed a text providing both a new theoretical foundation and practical guidance on how to approach the challenge of biodiversity decline in the Anthropocene. This is a global challenge demanding global approaches to understand its scope and implications. Until recently, we have simply lacked the tools to do so. We are now entering an era in which we can realistically begin to understand and monitor the multidimensional phenomenon of biodiversity at a planetary scale. This era builds upon three centuries of scientific research on biodiversity at site to landscape levels, augmented over the past two decades by airborne research platforms carrying spectrometers, lidars, and radars for larger-scale observations. Emerging international networks of fine-grain in-situ biodiversity observations complemented by space-based sensors offering coarser-grain imagery—but global coverage—of ecosystem composition, function, and structure together provide the information necessary to monitor and track change in biodiversity globally. This book is a road map on how to observe and interpret terrestrial biodiversity across scales through plants—primary producers and the foundation of the trophic pyramid. It honors the fact that biodiversity exists across different dimensions, including both phylogenetic and functional. Then, it relates these aspects of biodiversity to another dimension, the spectral diversity captured by remote sensing instruments operating at scales from leaf to canopy to biome. The biodiversity community has needed a Rosetta Stone to translate between the language of satellite remote sensing and its resulting spectral diversity and the languages of those exploring the phylogenetic diversity and functional trait diversity of life on Earth. By assembling the vital translation, this volume has globalized our ability to track biodiversity state and change. Thus, a global problem meets a key component of the global solution. The editors have cleverly built the book in three parts. Part 1 addresses the theory behind the remote sensing of terrestrial plant biodiversity: why spectral diversity relates to plant functional traits and phylogenetic diversity. Starting with first principles, it connects plant biochemistry, physiology, and macroecology to remotely sensed spectra and explores the processes behind the patterns we observe. Examples from the field demonstrate the rising synthesis of multiple disciplines to create a new cross-spatial and spectral science of biodiversity. Part 2 discusses how to implement this evolving science. It focuses on the plethora of novel in-situ, airborne, and spaceborne Earth observation tools currently and soon to be available while also incorporating the ways of actually making biodiversity measurements with these tools. It includes instructions for organizing and conducting a field campaign. Throughout, there is a focus on the burgeoning field of imaging spectroscopy, which is revolutionizing our ability to characterize life remotely. Part 3 takes on an overarching issue for any effort to globalize biodiversity observations, the issue of scale. It addresses scale from two perspectives. The first is that of combining observations across varying spatial, temporal, and spectral resolutions for better understanding—that is, what scales and how. This is an area of ongoing research driven by a confluence of innovations in observation systems and rising computational capacity. The second is the organizational side of the scaling challenge. It explores existing frameworks for integrating multi-scale observations within global networks. The focus here is on what practical steps can be taken to organize multi-scale data and what is already happening in this regard. These frameworks include essential biodiversity variables and the Group on Earth Observations Biodiversity Observation Network (GEO BON). This book constitutes an end-to-end guide uniting the latest in research and techniques to cover the theory and practice of the remote sensing of plant biodiversity. In putting it together, the editors and their coauthors, all preeminent in their fields, have done a great service for those seeking to understand and conserve life on Earth—just when we need it most. For if the world is ever to construct a coordinated response to the planetwide crisis of biodiversity loss, it must first assemble adequate—and global—measures of what we are losing

Responses and mechanisms of a mediterranean grassland ecosystem to nutrient addition

Doutoramento em Engenharia Florestal e dos Recursos Naturais - Instituto Superior de AgronomiaGlobal changes, resulting from anthropogenic activities, are increasing precipitation variability, drought and nutrient inputs into ecosystems. These global change drivers are expected to induce changes in grassland species richness and composition and functional structure and diversity which may in turn affect ecosystem functioning. This is particularly important for the Mediterranean basin, a climate change hotspot. Understanding how these changes affect grassland structure and functioning is critical to anticipate impacts of global change, improve management actions and develop land management strategies and restoration tools to mitigate grassland degradation. Through a pot greenhouse experiment, we applied three levels of extended autumn drought and two levels of nitrogen deposition to grassland communities. The severe drought originated a shorter growing season, and led to lower net ecosystem exchange and gross primary productivity, which translated into reduced productivity. Drought induced changes in functional group proportion and delayed plant phenology. Nitrogen addition did not affect productivity, diversity or phenology. However, nitrogen interacted with the severe drought treatment to attenuate the negative effects on total carbon fluxes. A 5-year nutrient addition field experiment was also conducted. Nitrogen, phosphorus and potassium were added in a factorial way to establish three treatments of one, two and three added nutrients, including controls. Grassland productivity was co-limited by multiple nutrients and precipitation. Nutrient addition decreased species richness and interacted with climatic variability to alter functional group composition. Resilience to disturbance was not affected by nutrient addition, as resistance that decreased with nutrient enrichment due to lower species richness was cancelled out by increased recovery due to the dominance of competitive graminoids. Community functional structure was affected by nutrient addition and precipitation and, for some traits, by their interaction and was the main determinant of productivity. Functional diversity was affected by precipitation, with drought negatively affecting community functional diversityN/

Ground, Proximal, and Satellite Remote Sensing of Soil Moisture

Soil moisture (SM) is a key hydrologic state variable that is of significant importance for numerous Earth and environmental science applications that directly impact the global environment and human society. Potential applications include, but are not limited to, forecasting of weather and climate variability; prediction and monitoring of drought conditions; management and allocation of water resources; agricultural plant production and alleviation of famine; prevention of natural disasters such as wild fires, landslides, floods, and dust storms; or monitoring of ecosystem response to climate change. Because of the importance and wide‐ranging applicability of highly variable spatial and temporal SM information that links the water, energy, and carbon cycles, significant efforts and resources have been devoted in recent years to advance SM measurement and monitoring capabilities from the point to the global scales. This review encompasses recent advances and the state‐of‐the‐art of ground, proximal, and novel SM remote sensing techniques at various spatial and temporal scales and identifies critical future research needs and directions to further advance and optimize technology, analysis and retrieval methods, and the application of SM information to improve the understanding of critical zone moisture dynamics. Despite the impressive progress over the last decade, there are still many opportunities and needs to, for example, improve SM retrieval from remotely sensed optical, thermal, and microwave data and opportunities for novel applications of SM information for water resources management, sustainable environmental development, and food security

Climate, land use and vegetation trends: Implication of land use change and climate change on northwestern drylands of Ethiopia

Land use / land cover (LULC) change assessment is getting more consideration by global environmental change studies as land use change is exposing dryland environments for transitions and higher rates of resource depletion. The semiarid regions of northwestern Ethiopia are not different as land use transition is the major problem of the region. However, there is no satisfactory study to quantify the change process of the region up to now. Hence, spatiotemporal change analysis is vital for understanding and identification of major threats and solicit solutions for sustainable management of the ecosystem. LULC change studies focus on understanding the patterns, processes and dynamics of land use transitions and driving forces of change. The change processes in dryland ecosystems can be either seasonal, gradual or abrupt changes of random or systematic change processes that result in a pattern or permanent transition in land use. Identification of these processes of change and their type supports adoption of monitoring options and indicate possible measures to be taken to safeguard this dynamic ecosystem. This study examines the spatiotemporal patterns of LULC change, temporal trends in climate variables and the insights of the communities on change patterns of ecosystems. Landsat imagery, MODIS NDVI, CRU temperature, TAMSAT rainfall and socio-ecological field data were used in order to identify change processes. LULC transformation was monitored using support vector machine (SVM) algorithm. A cross-tabulation matrix assessment was implemented in order to assess the total change of land use categories based on net change and swap change. In addition, the pattern of change was identified based on expected gain and loss under a random process of gain and loss, respectively. Breaks For Additive Seasonal and Trend (BFAST) analysis was employed for determining the time, direction and magnitude of seasonal, abrupt and trend changes within the time series datasets. In addition, Man Kendall test statistic and Sen’s slope estimator were used for assessing long term trends on detrended time series data components. Distributed lag (DL) model was also adopted in order to determine the time lag response of vegetation to the current and past rainfall distribution. Over the study period of 1972- 2014, there is a significant change in LULC as evidenced by a significant increase in size of cropland of about 53% and a net loss of over 61% of woodland area. The period 2000-2014 has shown a sharp increase of cropland and a sharp decline of woodland areas. Proximate causes include agricultural expansion and excessive wood harvesting; and underlying causes of demographic factor, economic factors and policy contributed the most to an overuse of existing natural resources. In both the observed and expected proportion of random process of change and of systematic changes, woodland has shown the highest loss compared to other land use types. The observed transition and expected transition under random process of gain of woodland to cropland is 1.7%, implies that cropland systematically gains to replace woodland. The comparison of the difference between observed and expected loss under random process of loss also showed that when woodland loses cropland systematically replaces it. The assessment of magnitude and time of breakpoints on climate data and NDVI showed different results. Accordingly, NDVI analysis demonstrated the existence of breakpoints that are statistically significant on the seasonal and long term trends. There is a positive trend, but no breakpoints on the long term precipitation data during the study period. The maximum temperature also showed a positive trend with two breakpoints which are not statistically significant. On the other hand, there is no seasonal and trend breakpoints in minimum temperature, though there is an overall positive trend along the study period. The Man-Kendall test statistic for long term average Tmin and Tmax showed significant variation where as there is no significant trend within the long term rainfall distribution. The lag regression between NDVI and precipitation indicated a lag of up to forty days. This proves that the vegetation growth in this area is not primarily determined by the current precipitation rather with the previous forty days rainfall. The combined analysis showed declining vegetation productivity and a loss of vegetation cover that contributed for an easy movement of dust clouds during the dry period of the year. This affects the land condition of the region, resulting in long term degradation of the environmen

Can indices of landscape function analysis (LFA) be derived from ground-based spectroscopy? A case study from gold mines on the Highveld of South Africa

The Minerals and Petroleum Resources Act (MRPDA) No 28 of 2002 of South Africa states that the holder of a mining permit remains liable for environmental consequences until a closure certificate has been issued, but does not stipulate the environmental standards required to obtain such a certificate. Monitoring of surface mining environments requires a consistent, repeatable and efficient method of monitoring that can be applicable to heterogeneous landscapes on large properties. To this end, this study forms a component towards the development and local testing of an internationally accepted, monitoring toolkit to monitor mine rehabilitation. Landscape Function Analysis (LFA) is a technique to rapidly determine broad biogeochemical processes occurring at the soil surface in heterogeneous landscapes. However, LFA is time consuming. Hyperspectral remote sensing (HSRS) is an alternative technique for monitoring large landscapes and is sensitive to both plant response to stress and soil minerals. The aim of this study is to derive LFA indices from HSRS (i.e. surface reflectance) data acquired with a hand-held spectrometer in order to predict landscape condition on deep-level gold mining surface environments in the Highveld region. The first objective was to test the potential of Partial Least Squares Regression (PLSR) modelling to predict LFA indices from the spectral data. The second objective was to test the potential for using Vegetation Indices (VI), calculated from hyperspectral data, to predict LFA indices. Twenty-three VIs, covering plant pigments (i.e. chlorophyll, carotenoids and anthocyanins), plant structural components (cellulose and lignin) and plant water content, were tested. The study was carried out in winter (dry season) as this is the season when disturbance is most visible, and both seasonal (deciduous) vegetation growth and annual species are absent. The study was carried out at two gold and uranium mining operations in the Highveld grassland biome: West Wits Operations near Carletonville (Gauteng Province) and Vaal River Operations near Klerksdorp (North West Province). At Vaal River, data was collected from high and low disturbance sites replicated three times, in each of four of the dominant vegetation types: wet grasslands, non-rocky grasslands, rocky grasslands and woody shrub sites representing increasing structural complexity. At West Wits Operations (n = 6 sampling plots), only non-rocky grasslands were sampled. Twenty five circular quadrats of 50 cm diameter were evenly distributed on five gradsects within each plot (Total quadrats = 750). Paired data acquired from each quadrat were reflectance data (44 cm field of view), LFA data (50 cm circular quadrat), and a photograph for later allocation of the remaining LFA data. Time constraints collecting LFA data reduced the total number of quadrats sampled in the field from 750 quadrats to 150 quadrats. Difficulties in accurately pairing the LFA and HSRS data further reduced the number of quadrats I used for statistical analyses to 105.The results of ranking the three LFA indices showed that stability was above the threshold value for sustainability, while infiltration was below threshold and nutrient cycling was close to threshold for all vegetation types and disturbance levels combined. These results suggest that soils were crusted and promoting run-off, and that disturbance was mainly impacting the vegetation component, rather than the soil component of the landscape. A comparison of non-rocky grasslands between the two mining regions showed that West Wits had higher LFA indices for infiltration and nutrient cycling (t-test, P ≤ 0.01, DF = 36.8 and 26.4 respectively) than Vaal River. All three LFA indices: stability, infiltration and nutrient cycling, differed between vegetation types (One-way ANOVA, P < 0.05, DF = 3, 101) with wet grasslands having consistently higher LFA indices than the other three vegetation types. Disturbance levels, combining vegetation types and mining region, also differed (t-tests, P < 0.01, DF = 81.8, 102.3 and 100.08 for stability, infiltration and nutrient cycling respectively), with high disturbance quadrats having lower LFA indices than low disturbance quadrats. When comparing LFA indices between disturbance levels within each vegetation type, low disturbance sites generally still had higher LFA indices than high disturbance sites (P < 0.05). These findings support the initial selection of distinct vegetation types and disturbance levels, with exceptions to this pattern believed to be a result of low replication (n = 5) for these vegetation types. The twenty-three VIs were not useful for predicting LFA indices from HSRS data under my experimental conditions. All the VIs had generally low indices as expected (in the case of chlorophyll and plant water-based VIs) for winter senesced Highveld grasses. All linear regressions between LFA indices and VIs had very weak coefficients of determination (r2 < 26%). The lignin index (NDLI) had the strongest coefficient of determination for both the stability (r2 = 25%, P < 0.01) and the nutrient cycling indices (r2 = 25%, P < 0.01). The infiltration index had the strongest coefficient of determination with the standard normalised difference vegetation index (NDVI) (r2 = 16%, P < 0.01). VIs had generally very low indices due to the winter senesced state of the Highveld vegetation. PLSR modelling produced much stronger regression coefficients of determination than did the VIs. The best PLSR model was a 15-component model to predict nutrient cycling (r2 = 54%, P < 0.01). A 13-component model predicting stability had an r2 = 38 % (P < 0.01), while a 17-component model was derived for infiltration (r2 = 32%, P < 0.01). In all three cases, these models were able to account for more than 90% of the spectral variability within the first two components. However, more than 16 components were required to account for 90% of the variability in the LFA measurements. It may be possible to reduce the number of components required for the PLSR modelling of the latter with a more standardised approach to the LFA data collection, i.e. having one observer who acquires all the LFA data in the field, and increased replication